<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Data Science on Manita Pote</title><link>https://manitapote.github.io/tags/data-science/</link><description>Recent content in Data Science on Manita Pote</description><generator>Hugo -- 0.147.2</generator><language>en</language><lastBuildDate>Mon, 01 Jan 2024 00:00:00 +0000</lastBuildDate><atom:link href="https://manitapote.github.io/tags/data-science/index.xml" rel="self" type="application/rss+xml"/><item><title>How to Handle Extremely Imbalanced Datasets</title><link>https://manitapote.github.io/blogs/imbalanced-dataset/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://manitapote.github.io/blogs/imbalanced-dataset/</guid><description>A guide to undersampling techniques for handling imbalanced datasets, including prototype generation, prototype selection, and cleaning methods.</description></item></channel></rss>